Entropy for End-Point and FFT-Based Binding Free Energy Calculations

用于端点和基于 FFT 的结合自由能计算的熵

• 批准号: 9752373
• 负责人: David Do Le Minh
• 金额: $ 33.11万
• 依托单位: ILLINOIS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9752373
• 关键词: Accounting; Affinity; Algorithms; Amino Acids; Antibiotics; Bacteria; Benchmarking; Binding; Binding Proteins; Biochemical; Biological; Biological Assay; Biological Process; Blinded; Charge; Chemistry; Code; Communities; Complex; Computer Assisted; Computer software; Computers; Computing Methodologies; Crystallization; Data Set; Docking; Drug Design; Drug Screening; Endocrine Disruptors; Entropy; Enzymes; Fourier Transform; Free Energy; Genetic Variation; Human; Life; Ligands; Measures; Metabolic; Methods; Missense Mutation; Molecular; Molecular Conformation; Mutation; Occupations; Pathway interactions; Performance; Pharmaceutical Preparations; Protein p53; Proteins; Research; Sampling; Science; Solvents; Statistical Mechanics; Structure; System; TP53 gene; Techniques; Testing; Translations; base; data resource; drug discovery; exhaustion; flexibility; follow-up; gain of function; improved; insight; mutant; open source; physical separation; prospective; protein complex; protein structure prediction; restraint; simulation; small molecule libraries; tumor growth; virtual

Project Summary Computers are often used to predict how tightly two molecules associate, their binding free energy. These predictions are helpful for designing drugs, predicting the consequences of genetic variation, and understanding how molecules interact to sustain life. Unfortunately, currently available methods are either fast or accurate, but not both. In general, fast methods do a poor job accounting for entropy, which is an important part of the free energy. The main objective of this project is to develop better ways to account for entropy in two popular techniques for studying molecular interactions: “end-point” simulations of the bound complexes and their unbound counterparts; and molecular docking based on the Fast Fourier Transform. Specifically, new ways to analyze calculation results will be derived, implemented, assessed, and optimized. Additionally, the methods will be combined with enhanced sampling techniques. Our new end-point and FFT-based methods will be assessed by their ability to reproduce benchmark results from slower but more accurate computational methods, as well as experimental results. The benchmark dataset will include protein-ligand complexes and protein-protein complexes with known binding affinities and crystal structures, as well as protein-protein complexes for which the effect of missense mutations on binding have been measured. We will also perform benchmark calculations on mutants of the tumor suppressor p53 that gain the ability to activate new proteins and promote tumor growth. In addition to serving as benchmarks, these calculations may provide mechanistic insight into how proteins bind various ligands and how p53 mutants gain new binding partners. Our new methods will also be tested in recurring community challenges: the “Drug Design Data Resource” (D3R) grand challenge to predict protein-ligand complex structures and affinities and the “Critical Assessment of PRediction of Interactions” (CAPRI) challenge for protein-protein structure prediction. These blinded challenges will allow for an unbiased comparison of our methods to those from other research groups. Finally, we will assess our methods in a drug discovery project. We will use established methods and our new methods to virtually screen a chemical library against a pair of structurally similar bacterial metabolic enzymes. One enzyme is relevant to active and the other to dormant bacteria. Compounds predicted to selectively bind the bacterial (opposed to human) enzymes will be experimentally tested in biochemical assays. We anticipate that our improved methods will be significantly more accurate than established approaches, advancing research ranging from interactome prediction to drug discovery.

项目摘要 计算机通常用于预测两个分子的结合自由能的紧密相关性。这些 预测有助于设计药物，预测遗传变异的后果和理解 分子如何相互作用以维持生命。不幸的是，当前可用的方法要么快或准确， 但不是两者。通常，快速方法对熵的核算很差，这是其中的重要组成部分 自由能。该项目的主要目的是开发更好的方法来说明两个流行的熵 用于研究分子相互作用的技术：绑定复合物及其它们的“终点”模拟 未绑定的对手；和分子对接基于快速傅立叶变换。特定的，新的方法 分析计算结果将得出，实施，评估和优化。另外，这些方法 将与增强的采样技术结合使用。 我们的新基于终点和基于FFT的方法将通过其重现基准结果的能力来评估 来自较慢但更准确的计算方法以及实验结果。基准数据集 将包括具有已知结合的蛋白质配合物和蛋白质 - 蛋白质复合物和晶体 结构以及蛋白质 - 蛋白质复合物，其识别突变对结合的影响 我们还将对获得肿瘤抑制剂P53的突变体进行基准计算 激活新蛋白并促进肿瘤生长的能力。除了用作基准之外 计算可以提供机械洞察蛋白如何结合各种配体以及p53突变体如何增益 新的约束伙伴。 我们的新方法还将在经常出现的社区挑战中进行测试：“药物设计数据资源” （D3R）预测蛋白质配体的复杂结构和事件的巨大挑战，以及“对 相互作用的预测”（CAPRI）对蛋白质 - 蛋白质结构预测的挑战。这些盲目的挑战 将允许将我们的方法与其他研究小组的方法进行公正的比较。 最后，我们将在药物发现项目中评估我们的方法。我们将使用既定的方法和我们的新方法 虚拟化学文库与一对结构相似的细菌代谢酶进行筛选的方法。 一种酶与活性有关，另一个与休眠细菌有关。预测将选择性结合的化合物 细菌（与人）酶将在生化测定中进行实验测试。我们期待 我们改进的方法将比建立的方法更加准确，从而推进研究 从相互作用的预测到药物发现。